Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks.
Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks.
复制标题
将代谢物浓度包括到通量平衡分析中：热力学可实现性作为对代谢网络中通量分布的限制。
DOI：
10.1186/1752-0509-1-23
复制
发表时间：
2007-06-01
影响因子：
--
通讯作者：
Holzhutter, Hermann-Georg
中科院分区：
文献类型：
--
作者：
Hoppe, Andreas;Hoffmann, Sabrina;Holzhutter, Hermann-Georg
近年来，约束优化-通常被称为通量平衡分析（FBA）-已成为一种广泛应用的方法，用于计算大规模代谢网络中的稳态通量。与动力学建模相比，FBA的显著优势在于它基本上只需要网络化学计量的知识。另一方面，FBA的结果在很大程度上是假设性的，因为该方法依赖于看似合理但难以证明的最优性原则，这些原则被认为是控制代谢通量分布的原则。为了增加基于FBA的通量计算的可靠性，我们提出了一个额外的侧约束，保证热力学实现，即通量方向是一致的吉布斯自由能的相应变化。后者取决于代谢物水平，从实验数据可以推断出合理的范围。在计算上，我们的方法的结果在一个混合整数线性优化问题的解决方案与二次评分函数。确定最佳通量分布以及代谢物谱，这确保了代谢物水平与其预期值的最小偏差的热力学可实现性。我们将我们的新方法应用于两个不同复杂性的示例性代谢网络，红细胞的代谢核心网络（30个反应）和大肠杆菌的代谢网络iJR 904（931个反应）。我们的计算表明，随着网络复杂性的增加，预测通量分布对标准吉布自由能变化和代谢物浓度范围变化的敏感性也会增加。我们证明了我们的方法用于评估临界浓度的外部代谢物，防止达到代谢稳态的有用性。我们的方法将通量方向和代谢物浓度之间的热力学联系纳入一个实用的计算算法。克服了传统FBA依赖于关于生化反应可逆性的直观假设的弱点。这使得即使在代谢物浓度可能急剧改变的极端网络条件下（例如酶抑制、底物耗尽或终产物积累）也能够计算可靠的通量分布。
In recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions. To augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibb's free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of Escherichia coli (931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibb's free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state. Our method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered.